Open AccessDay‐ahead unit commitment model for microgrids
Author(s) -
Deckmyn Christof,
Van de Vyver Jan,
Vandoorn Tine L.,
Meersman Bart,
Desmet Jan,
Vandevelde Lieven
Publication year - 2017
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2016.0222
Subject(s) - microgrid , power system simulation , schedule , scheduling (production processes) , time horizon , renewable energy , heuristics , energy storage , computer science , energy management system , automotive engineering , electric power system , reliability engineering , energy management , mathematical optimization , operations research , engineering , power (physics) , operations management , electrical engineering , energy (signal processing) , physics , mathematics , quantum mechanics , statistics , operating system
In this study, a heuristics‐based optimisation methodology for a day‐ahead unit commitment (UC) model in microgrids is proposed. The model aims to schedule the power among the different microgrid units while minimising the operating costs together with the CO 2 emissions produced. A storage device is added where the charge and discharge schedule is calculated according to both objectives. In addition, as a part of the demand side participation strategy, a charging schedule was determined for the electric vehicles (EV) in order to increase the system security and further reduce the costs and emissions. A congestion management approach is also introduced, which eliminates congestions by effective unit scheduling according to congestion signals provided by the distribution system operators. The complete day‐ahead time horizon is divided in 96 time steps (each with a 15 min time span), which makes the UC problem more complicated. The studied system includes renewable energy resources, a storage unit, two microturbines, a fuel cell and EVs. The results demonstrate that the proposed model is robust and is able to reduce the microgrid operating costs and emissions by optimal scheduling of the microgrid units, and is able to take into account local congestion problems.